Oncology/Hematology

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.

BACKGROUND: Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. Neighborhood component analys...

Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging.

Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a st...

AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy.

The era of real-time radiotherapy is upon us. Robotic and gimbaled linac tracking are clinically established technologies with the clinical realizatio...

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatme...

A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a thera...

Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition.

Three-dimensional in vitro tumor models provide more physiologically relevant responses to drugs than 2D models, but the lack of proper evaluation ind...

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimat...

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor cl...

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploi...

The risk of conversion in minimally invasive oncological abdominal surgery. Meta-analysis of randomized evidence comparing traditional laparoscopic versus robot-assisted techniques.

PURPOSE: The objective of this study was to investigate the risk of conversion associated with conventional laparoscopic surgery (LAP) versus robot-as...

Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non-muscle-invasive bladder cancer with CT.

OBJECTIVE: To construct a deep-learning convolution neural network (DL-CNN) system for the differentiation of muscle-invasive bladder cancer (MIBC) an...

Deep generative neural network for accurate drug response imputation.

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tu...

Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.

Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage o...

Hypervascular Nodules and Stiffer Liver are Associated with Recurrence after Microwave Ablation in Patients with Hepatocellular Carcinoma: A Double-Center Analysis.

 The aim of this study was to detect the most important risk factors for recurrence after microwave ablation (MWA) of hepatocellular carcinoma (HCC)....

Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.

OBJECTIVES: To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imagi...

Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC.

OBJECTIVES: To develop and evaluate a deep learning-based model capable of detecting primary hepatic malignancies in multiphase CT images of patients ...

Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.

BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep l...

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on F-FDG PET/CT and machine learning.

PURPOSE: To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on F-FDG PET/CT and radiomi...

Integrated multi-omics analysis of ovarian cancer using variational autoencoders.

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics ana...

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and n...